Al-Bugharbee et al., 2016 - Google Patents
A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modellingAl-Bugharbee et al., 2016
View PDF- Document ID
- 4292188868778297043
- Author
- Al-Bugharbee H
- Trendafilova I
- Publication year
- Publication venue
- Journal of Sound and Vibration
External Links
Snippet
This study proposes a methodology for rolling element bearings fault diagnosis which gives a complete and highly accurate identification of the faults present. It has two main stages: signals pretreatment, which is based on several signal analysis procedures, and diagnosis …
- 238000000034 method 0 title abstract description 105
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00496—Recognising patterns in signals and combinations thereof
- G06K9/00536—Classification; Matching
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
- G06K9/6284—Single class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Al-Bugharbee et al. | A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling | |
Zhang et al. | Bearing fault diagnosis via generalized logarithm sparse regularization | |
Chen et al. | A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks | |
Tabrizi et al. | Early damage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine | |
Chegini et al. | Application of a new EWT-based denoising technique in bearing fault diagnosis | |
Liu et al. | A two-stage approach for predicting the remaining useful life of tools using bidirectional long short-term memory | |
Cerrada et al. | A review on data-driven fault severity assessment in rolling bearings | |
Soualhi et al. | Pattern recognition method of fault diagnostics based on a new health indicator for smart manufacturing | |
Zhang et al. | Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine | |
Saidi et al. | Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis | |
Hong et al. | Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method | |
Hu et al. | Bearing performance degradation assessment based on optimized EWT and CNN | |
Zheng et al. | Refined time-shift multiscale normalised dispersion entropy and its application to fault diagnosis of rolling bearing | |
Żak et al. | Data-driven vibration signal filtering procedure based on the α-stable distribution | |
Du et al. | Fault diagnosis using adaptive multifractal detrended fluctuation analysis | |
Li et al. | Canonical correlation analysis of dimension reduced degradation feature space for machinery condition monitoring | |
Zhan et al. | Robust detection of gearbox deterioration using compromised autoregressive modeling and Kolmogorov–Smirnov test statistic—Part I: Compromised autoregressive modeling with the aid of hypothesis tests and simulation analysis | |
Zhang et al. | Health indicator based on signal probability distribution measures for machinery condition monitoring | |
Lu et al. | Bearing fault diagnosis based on clustering and sparse representation in frequency domain | |
Jaber et al. | A simulation of non-stationary signal analysis using wavelet transform based on LabVIEW and Matlab | |
Al-Bugharbee et al. | A new methodology for fault detection in rolling element bearings using singular spectrum analysis | |
Skowronek et al. | Assessment of background noise properties in time and time–frequency domains in the context of vibration-based local damage detection in real environment | |
Attoui | Novel fast and automatic condition monitoring strategy based on small amount of labeled data | |
Heydarzadeh et al. | Gearbox fault diagnosis using power spectral analysis | |
Chemseddine et al. | Gear fault feature extraction and classification of singular value decomposition based on Hilbert empirical wavelet transform |